|About this Abstract
||MS&T21: Materials Science & Technology
||Ceramics and Glasses Modeling by Simulations and Machine Learning
||Machine Learning as a Tool to Accelerate the Design of Nuclear Waste Glasses with Enhanced Sulfur Loadings
||Taihao Han, Xinyi Xu, Jie Huang, Albert A. Kruger, Aditya Kumar, Ashutosh Goel
|On-Site Speaker (Planned)
The nuclear waste with a high concentration of alkali/alkaline-earth sulfates is vitrificated with the direct feed approach. It is difficult for the existing empirical models to predict sulfate solubility in these glasses or design glass formulations with enhanced sulfate loadings, especially for HLW glasses whose composition falls outside of the range encompassed by the database used to develop/calibrate the models. This study harnesses the power of artificial intelligence with a goal to address the limitations of the existing models. Random Forests model is trained using a large database; comprising >1000 waste glasses and encompassing a wide range of glass compositions and processing variables. Next, the RF model is used to quantitatively assess the influence of glasses’ compositional/processing variables on the SO3 solubility loading. Finally, on the premise of such understanding of influential variables, two closed-form analytical models –one highly-parametrized and one with fewer input variables – are developed.